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IEEE Transactions on Sustainable Energy
Journal Prestige (SJR): 2.318  Citation Impact (citeScore): 7 Number of Followers: 15  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1949-3029 Published by IEEE  [228 journals]
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- IEEE Transactions on Sustainable Energy Publication Information
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Pages: C2 - C2
PubDate: WED, 20 SEP 2023 14:06:37 -04
Issue No: Vol. 14, No. 4 (2023)
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- IEEE Industry Applications Society Information
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Pages: C3 - C3
PubDate: WED, 20 SEP 2023 14:06:37 -04
Issue No: Vol. 14, No. 4 (2023)
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- IEEE Transactions on Sustainable Energy Information for Authors
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Pages: C4 - C4
PubDate: WED, 20 SEP 2023 14:06:37 -04
Issue No: Vol. 14, No. 4 (2023)
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- Source-Load Scenario Generation Based on Weakly Supervised Adversarial
Learning and Its Data-Driven Application in Energy Storage Capacity Sizing
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Authors: Qian Wang;Xueguang Zhang;Dianguo Xu;
Pages: 1918 - 1932
Abstract: The historical measured data of renewable energy sources and loads can be processed in various ways to generate scenarios for energy storage planning. With the development of advanced forecast technology, the valuable reference of massive forecast data accumulated by the prediction platform in scenario generation is ignored. To this end, we propose a new paradigm based on scenario generation for energy storage planning considering source-load uncertainties. First, a novel generative adversarial network (GAN) is constructed under weakly supervised learning. The network can extract the prediction errors between predicted data and measured data to generate source-load profiles. The loss function of the network is redesigned to adapt to the data generation task. To match the planning process, scenario generation and reduction algorithms are embedded in the GAN. Then, an energy storage sizing model considering battery health constraints is established. Case studies show that the proposed method can accurately portray the local fluctuation characteristics of source-load power and properly reduce conservatism compared with the model-driven method.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Optimization of Communication Network for Distributed Control of Wind Farm
Equipped With Energy Storages-
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Authors: Ji Han;Wenxi Lyu;Huihui Song;Yanbin Qu;Zhiquan Wu;Xin Zhang;Yingying Li;Zhe Chen;
Pages: 1933 - 1949
Abstract: Consensus based distributed control schemes have been widely applied to different operation scenarios of wind farms (WFs). However, few studies concern the influences of WF communication network topology to the consensus control performances. This paper proposes an optimal design method of the WF communication network for the consensus based re/active power regulation control of the WF, in which each individual wind turbine (WT) is equipped with an energy storage (ES) unit. Firstly, the basic model of the WT equipped with ES is presented, and the consensus based control scheme of the power regulation is described. Secondly, the optimization model for the communication network is constructed, so as to speed up the convergence rate, improve the communication survivability and reduce the communication links in the control. Thirdly, this paper proposes a bi-level alternating direction method of multipliers (ADMM) based solving framework, which decomposes the optimization into sub-problems. Finally, the performances of the control using the designed optimal communication network are tested and compared with other networks in the control, and the results verify the superiority of the proposed method.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Parameter Optimization for Var Planning of Systems With High Penetration
of Wind Power: An Adaptive Equivalent Reduction Method-
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Authors: Lin Xue;Tao Niu;Sidun Fang;Zhengshuo Li;
Pages: 1950 - 1963
Abstract: Voltage-related issues such as dramatic voltage fluctuation, lack of Var reserves, become more prominent in high wind power penetration (HWPP) system, leading to increasing of large-scale cascading trip risks within 100–200 ms. During the cascading process, fast dynamic Var support, which is strongly related to internal parameters of Var devices, is quite crucial for HWPP system security. If device parameters optimization is also considered in the Var planning, it will be more promising to achieve less investment cost and guarantee transient security for HWPP system. However, the complex control elements and numerous internal parameters optimization cause a huge computational burden of planning issues. Therefore, this paper proposes an adaptively parameter order equivalent reduced optimization (APO-ERO) approach to efficiently and accurately solve the Var planning problem with parameter allocation. First, the original high-order Var device model is adaptively reduced to an optimal low-order model that minimizes the reduced order error. Next, an equivalent low-order optimization model in the upper layer and an inverse mapping parameter allocation model in the lower layer are established to relieve the computational burden. Finally, the two different test systems with HWPP verify that the proposed method can improve computational efficiency on the premise of guaranteeing accuracy.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Optimal Transmission Switching With Uncertainties From Both Renewable
Energy and N-K Contingencies-
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Authors: Tong Han;David J. Hill;Yue Song;
Pages: 1964 - 1978
Abstract: This article focuses on the $N-k$ security-constrained optimal transmission switching (OTS) problem for variable renewable energy (VRE) penetrated power grids. A new three-stage stochastic and distributionally robust OTS model is proposed. The first stage has the primary purpose to schedule the power generation and network topology based on the forecast of VRE. The second stage controls the power generation and voltage magnitudes of voltage-controlled buses in response to VRE uncertainty, and the third stage reacts to $N!-!k$ contingencies additionally by line switching and load shedding. The VRE and $N!-!k$ contingencies, considering different availability of their probability distributions, are tackled by stochastic and distributionally robust optimization, respectively. By adopting stage-wise realization of uncertainties in VRE and contingencies, the associated corrective controls with different mechanisms can be handled separately and properly, which makes the proposed OTS model more realistic than existing two-stage ones. For solving the proposed OTS model, its tractable reformulation is derived, and a solution approach that combines the nested column-and-constraint generation algorithm and Dantzig–Wolfe procedure is developed. Finally, case studies include a simple IEEE network for illustrative purposes and then real system networks to demonstrate the efficacy of the proposed approach.
PubDate: WED, 20 SEP 2023 14:07:59 -04
Issue No: Vol. 14, No. 4 (2023)
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- Solar-Mixer: An Efficient End-to-End Model for Long-Sequence Photovoltaic
Power Generation Time Series Forecasting-
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Authors: Ziyuan Zhang;Jianzhou Wang;Yurui Xia;Danxiang Wei;Yunbo Niu;
Pages: 1979 - 1991
Abstract: The expansion of photovoltaic power generation makes photovoltaic power forecasting an essential requirement. With the development of deep learning, more accurate predictions have become possible. This paper proposes an efficient end-to-end model for solar power generation that allows for long-sequence time series forecasting. Two modules comprise the forecasting model: the anomaly detection module and the forecasting module. Singular values are detected and corrected by the anomaly detection module. And in the forecasting module, the series is first divided into different intervals. After that, the divided data is passed through a decomposition module embedded in the neural network, and each interval is encoded as high-dimension vectors. These vectors are fed into the mixing layer, which is used to learn the relationship between intervals and channels after the encoding process. Every mixing layer comprises two different layers: the channel-mixing layer allows communication between different channels, and the interval-mixing layer allows communication between different intervals. This neural network is purely dependent on the multilayer perceptron, resulting in a network with low system latency and low training cost. The experiments results not only show the model proposed in this paper can make accurate long-sequence time series forecasts for photovoltaic power generation at three sites but also show that the model can defeat the state-of-the-art model in long-sequence photovoltaic power generation time series forecasting work.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Energy Management in Integrated Energy System Using Energy–Carbon
Integrated Pricing Method-
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Authors: Yudong Wang;Junjie Hu;Nian Liu;
Pages: 1992 - 2005
Abstract: The interdependence of different energy forms and flexible energy interaction among multiagents in an integrated energy system (IES) are significant for reducing carbon emissions. Therefore, optimizing the IES to achieve low-carbon emission and economic goals is necessary. This study proposes an IES energy management method based on the energy–carbon integrated pricing method. First, a consumption-based integrated pricing model is proposed to calculate the energy–carbon integrated prices of electricity, thermal resources, and gas for energy service provider (ESP). Second, an energy management method based on the Stackelberg game is established, with the ESP as the leader and the prosumers as the followers. In the game model, the objectives of the ESP and prosumers are to maximize profit by formulating an appropriate energy–carbon integrated pricing strategy and maximize consumer surplus by optimizing load, respectively. Finally, the effectiveness of the proposed method is verified using practical examples. The results indicate that the proposed method can increase the profit of ESP and reduce carbon emissions more efficiently than traditional methods.
PubDate: WED, 20 SEP 2023 14:07:59 -04
Issue No: Vol. 14, No. 4 (2023)
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- Risk-Oriented Operational Model for Fully Renewable Cooperative Prosumers
in a Modern Water-Energy Nexus Structure-
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Authors: Mohammadreza Daneshvar;Behnam Mohammadi-ivatloo;Amjad Anvari-Moghaddam;
Pages: 2006 - 2019
Abstract: The day-by-day increment in the demand for diverse types of energy together with the CO2-based climate concerns has intensified the need for innovative decarbonization plans leading the energy sector to produce clean, cost-effective, and reliable multi-energy. Herein, inevitable water and power interactions unlock significant benefits for the integrated energy network in the form of water-energy nexus models. In this work, a holistic water-energy nexus model is developed for the operation of cooperative prosumers equipped with 100% renewables in the modern interconnected energy structure. The model is empowered by the transactive energy technology to allow prosumers to cooperatively share multi-energy with each other for reliably serving power and water in a deregulated environment. The proposed model also benefits from hydrogen-based energy conversion units that not only improve the flexibility of prosumers in reliable energy supply but also increase their economic achievements by selling the produced gas to the gas grid. As prosumers are targeted for fully clean energy production, their contributions in the energy interactions are under the high level of risks associated with renewables’ intermittences. Due to this, a risk-averse stochastic operational model is proposed that enables the decision-maker to adopt optimal strategies against the uncertain fluctuations in the system. The effectiveness of the proposed model is examined considering prosumers located in Chicago, USA. According to the obtained results, the model can affordably facilitate the realization of Chicago's plans for achieving the goal of equipping with 100% renewable energy sources for a fully clean multi-energy generation.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Dynamic Coupling Analysis and Small-Signal Stability for Multi-Parallel
PLL-Synchronous VSC-Based Renewable Energy Plants During Asymmetrical LVRT
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Authors: Lei Guan;Jun Yao;
Pages: 2020 - 2035
Abstract: Studying the small-signal stability for multi-parallel voltage source converter (VSC)-based renewable energy plants (REPs) during the fault steady-state of low-voltage ride-through (LVRT) in weak grid is significant for designing a stabilized controller to ensure and improve success of LVRT. Different from the independent grid-connected system and symmetric condition, in the asymmetrical fault steady-state, there are more complicated dynamic interactions between the positive-sequence (PS) and negative-sequence (NS) systems as well as between the REPs in multi-fed grid-connected system. This study unveils the dynamic coupling mechanism between REPs by deduced small-signal model during asymmetric LVRT, which provides a solid foundation for designing the novel stabilized controllers. The influence of this dynamic coupling effect between REPs on the system's small-signal stability in the asymmetrical fault steady-state is analyzed. It's shown that the dynamic coupling between REPs will be aggravated by the deteriorating asymmetrical fault, and it will cause the existing stabilized controller's performance decreasing or even invalid. Combining with small-signal stability analysis, the asymmetric fault degree's influencing mechanism on small-signal stability of multi-fed grid-connected system is revealed from the perspective of self-damping and mutual damping. Finally, simulations and experiments are carried out to validate analysis results.
PubDate: WED, 20 SEP 2023 14:07:57 -04
Issue No: Vol. 14, No. 4 (2023)
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- Implications of Future Price Trends and Interannual Resource Uncertainty
on Firm Solar Power Delivery With Photovoltaic Overbuilding and Battery
Storage-
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Authors: Guoming Yang;Dazhi Yang;Chao Lyu;Wenting Wang;Nantian Huang;Jan Kleissl;Marc J. Perez;Richard Perez;Dipti Srinivasan;
Pages: 2036 - 2048
Abstract: Generation from solar is inherently variable. Through a strategic combination of excessive capacity expansion (i.e., overbuilding) and battery storage, the variable solar generation can be cost-effectively firmed up, in that, it is able to meet the required generation target with absolute certainty. Firming up solar generation implies additional cost, which can be quantified through the firm kWh premium. This paper proposes a new model for the optimization of firm kWh premium through either a mixed-integer linear program or a bilinear program, depending on whether a generic or detailed battery model is used. The (bi)linear-program formulation greatly reduces the complexity of the original iterative approach. Additionally, since the firm kWh premium is a function of photovoltaic and battery prices, we show how future price change can affect the economics of firm power delivery and whether true grid parity can be eventually achieved. Lastly, the sensitivity of the firm kWh premium to photovoltaic modeling uncertainty and inter-annual solar resource uncertainty is analyzed.
PubDate: WED, 20 SEP 2023 14:07:57 -04
Issue No: Vol. 14, No. 4 (2023)
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- Fast Frequency Regulation From a Wind Farm-BESS Unit by Model Predictive
Control: Method and Hardware-in-the-Loop Validation-
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Authors: Diego Cirio;Francesco Conte;Bruno Gabriele;Chiara Gandolfi;Stefano Massucco;Marco R. Rapizza;Federico Silvestro;
Pages: 2049 - 2061
Abstract: The integration of Renewable Energy Sources (RES) into the power system requires an upgrade of the adopted management and control solutions. Focusing on frequency regulation, the same RES, as well as loads, both possibly coupled with energy storage systems, are called to provide a contribution through suitable regulation services. In this context, this paper proposes a control strategy for enabling a unit composed by a Wind Farm and Battery Energy Storage Systems (BESSs) to provide a fast frequency regulation service. The control is based on Model Predictive Control technique, whereas the regulation service is defined according to the technical requirements of the Italian Transmission System Operator (TSO). The approach is also tested via Hardware-In-the-Loop simulations. More specifically, tests are carried out by implementing the control algorithm on a Raspberry Pi board that communicates with a real BESS and with a real-time simulator implementing a benchmark power system. The validation results prove the practical effectiveness of the proposed control method since they demonstrate that the designed algorithms can be implemented on a low-cost hardware and applied, without computational and communication issues, in a real-field framework.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Unified Dynamic Equivalent Model for Distributed Photovoltaic Generation
Systems With Different Fault-Ride-Through Strategies-
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Authors: Zhe Zhang;Siyang Liao;Yuanzhang Sun;Jian Xu;Deping Ke;Bo Wang;Rui Chen;
Pages: 2062 - 2078
Abstract: The grid penetration of distributed photovoltaic (DPV) energy is increasing; therefore producing equivalent models of DPV systems is critical for the dynamic analysis of power systems. In this article, a DPV system aggregation model based on interconnection point voltages and inverter types with a novel cluster and aggregation method is proposed. This model can obtain a similar power output to that obtained through simulation with a detailed model. Dynamic voltages of interconnection points and an equivalent admittance matrix were used as clustering indices in this study. These indices were weighted using an analytic hierarchy process model. Photovoltaic systems with different inverters, control strategies and fault-ride-through dynamics; grid connection points with varying electrical distances and voltage dynamics were unified in the developed model by ensuring that the power characteristics before and after aggregation were equivalent. The results of this study imply that the DPV systems in an area differ substantially in terms of output dynamics, and these dynamic features should be quantified during equivalent modeling. The proposed method obtained superior results to relevant previous methods that do not consider the fault ride throughs and voltage dynamics.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- A New Multi-Resolution Closed-Loop Wind Power Forecasting Method
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Authors: Maryam Nejati;Nima Amjady;Hamidreza Zareipour;
Pages: 2079 - 2091
Abstract: By the increasing number and size of wind farms, wind generation forecasting has become a basic requirement for their connection to the power grid; otherwise, power system operators and electricity market participants cannot make the right decisions and may incur significant costs and penalties. In this paper, a new multi-resolution closed-loop wind power forecasting method with a difference signal feedback loop is proposed. Within the proposed method, wind power is initially predicted in two different resolutions (such as with hourly and sub-hourly time steps) by two low/high-resolution pre-predictors and then the inconsistency between their predictions is measured through the difference signal. The generated difference signal is used as a guide for the two low/high-resolution wind power post-predictors. If their wind power forecasts are inconsistent, the difference signal is updated and used as the feedback for the low/high-resolution post-predictors. This closed-loop forecasting-updating process is iterated until the post-predictors reach consistent results. To evaluate the performance of the proposed multi-resolution closed-loop method, it is tested on two different real-world wind farms and the results are compared with the results of several other widely used/recently published wind power forecast methods using various error metrics and different forecast horizons.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Risk-Aware Operating Regions for PV-Rich Distribution Networks Considering
Irradiance Variability-
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Authors: Edgar Mauricio Salazar Duque;Juan S. Giraldo;Pedro P. Vergara;Phuong H. Nguyen;Anne van der Molen;J. G. Slootweg;
Pages: 2092 - 2108
Abstract: This article proposes a framework to identify, visualize, and quantify risk of potential over/under voltage due to annual energy consumption and PV generation growth. The stochastic modeling considers the following: (i) Active and reactive power profiles for distribution transformers, dependent on annual energy consumption and activity in the serviced areas. (ii) Variable solar irradiance profiles that allow a broader range of PV generation scenarios for sunny, overcast, and cloudy days. The proposed framework uses multivariate-$t$ copulas to model temporal correlations between random variables to generate synthetic scenarios. A probabilistic power flow is computed using the generated scenarios to define critical static operating regions. Results show that classical approaches may underestimate the maximum PV capacity of distribution networks when local irradiance conditions are not considered. Moreover, it is found that including annual energy consumption growth is critical to establishing realistic PV installation capacity limits. Finally, a sensitivity analysis shows that taking a 5% of overvoltage risk could increase up to 15% of the PV installed capacity limits.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Day-Ahead Parametric Probabilistic Forecasting of Wind and Solar Power
Generation Using Bounded Probability Distributions and Hybrid Neural
Networks-
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Authors: Theodoros Konstantinou;Nikos Hatziargyriou;
Pages: 2109 - 2120
Abstract: The penetration of renewable energy sources in modern power systems increases at an impressive rate. Due to their intermittent and uncertain nature, it is important to forecast their generation including its uncertainty. In this article, an ensemble artificial neural network is applied for day ahead solar and wind power generation parametric probabilistic forecasting. The proposed architecture includes two components: a sub-models component and a Meta-Learner component. The first component includes an ensemble of artificial neural networks that have the ability to estimate the parameters of an underlying probability distribution. The Meta-Learner is responsible for grouping the training samples based on the estimated level of generation, through a classification-clustering process and use the output of the corresponding sub-models to calculate the final parametric probabilistic estimation. The proposed model is compared to both parametric and non-parametric state of the art probabilistic techniques for solar and wind power generation forecasting, exhibiting superior performance.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Wind Power Curve Modeling With Large-Scale Generalized Kernel-Based
Regression Model-
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Authors: Yun Wang;Xiaocong Duan;Dongran Song;Runmin Zou;Fan Zhang;Yifen Li;
Pages: 2121 - 2132
Abstract: Accurate wind power curves (WPCs) are crucial for wind energy development and utilization, e.g., wind power forecasting and wind turbine condition monitoring. In the era of Big Data, large-scale datasets make the training of power curve models inefficient, especially for kernel-based models. Furthermore, most models do not take into account the error characteristics of WPC modeling. In this study, a large-scale generalized kernel-based regression model is proposed to solve the above problem. First, a generalized loss function, which can model both symmetric and asymmetric error distributions, is designed for model training. Then, the Nyström technique is employed to get the approximate kernel matrix, based on which an eigenvalue-based kernel regression framework is constructed. Next, a large-scale generalized kernel-based regression model is developed with model parameters tuned using the alternating direction method of multipliers. Before WPC modeling, a three-step data processing method based on isolation forest is designed to process missing data, irrational data, and outliers in the collected data. The WPC modeling results on four large-scale wind datasets demonstrate that the proposed model generates accurate WPCs with high efficiency. Furthermore, the effect of turbulence intensity on WPC modeling and the effectiveness of LSGKRM with multivariate inputs are also verified.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Uncertainty-Aware Trading of Congestion and Imbalance Mitigation Services
for Multi-DSO Local Flexibility Markets-
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Authors: Ángel Paredes;José A. Aguado;Pedro Rodríguez;
Pages: 2133 - 2146
Abstract: The design of Local Flexibility Markets (LFMs) for energy and reserve dispatch of Renewable Distributed Energy Resources (RDERs) has recently been a topic of wide research. However, in an scenario with high penetration of RDERs deployed among different Distribution System Operators (DSOs) jurisdictions, further operational requirements concerning congestion and imbalance mitigation services may lay down. In this context, the relationship between capacity and energy products and the uncertainty management scheme becomes essential for procuring of RDERs flexibility. This paper proposes an uncertainty-aware Multi-DSO LFM. This market setting uses flexibility products to mitigate congestions and imbalances among different DSOs. First, capacity products hold back the flexibility of the RDERs in anticipation of contingencies. Then, energy products are activated within each time slot if the event finally occurs. LFM is solved in a coordinated and decentralised fashion using the properties of the Alternating Direction Method of Multipliers (ADMM), preserving participants' privacy. Uncertainty of RDERs and energy events duration are modelled using chance-constraint linear optimisation. The proposed methodology has been tested in a case study based on a realistic dataset and radial distribution systems.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Distributed Event-Triggered Current Sharing Control for Islanded DC
Microgrids With Quantized State-
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Authors: Xiao-Kang Liu;Jiong Cai;Lantao Xing;Yan-Wu Wang;
Pages: 2147 - 2156
Abstract: To eliminate voltage deviation of DC microgrids induced by droop control, distributed secondary control has been widely developed with lots of variants. Compared with most existing approaches, a digital communication network is considered where signals are quantized before transmitting into the channel. Then, a distributed secondary control strategy based on quantized signals is designed, to achieve current sharing and voltage restoration under a limited communication width. In this regard, the communication burden can be greatly reduced by using fewer information bits. For further reducing controller updating, we integrate signal quantization with an event-triggered mechanism. Finally, simulation and experimental results show the effectiveness of the proposed method.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Control Co-Design of Power Take-Off Systems for Wave Energy Converters
Using WecOptTool-
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Authors: Carlos A. Michelén Ströfer;Daniel T. Gaebele;Ryan G. Coe;Giorgio Bacelli;
Pages: 2157 - 2167
Abstract: Improved power take-off (PTO) controller design for wave energy converters is considered a critical component for reducing the cost of energy production. However, the device and control design process often remains sequential, with the space of possible final designs largely reduced before the controller has been considered. Control co-design, whereby the device and control design are considered concurrently, has resulted in improved designs in many industries, but remains rare in the wave energy community. In this paper we demonstrate the use of a new open-source code, WecOptTool, for control co-design of wave energy converters, with the aim to make the co-design approach more accessible and accelerate its adoption. Additionally, we highlight the importance of designing a wave energy converter to maximize electrical power, rather than mechanical power, and demonstrate the co-design process while modeling the PTO's components (i.e., drive-train and generator, and their dynamics). We also consider the design and optimization of causal fixed-structure controllers. The demonstration presented here considers the PTO design problem and finds the optimal PTO drive-train that maximizes annual electrical power production. The results show a 22% improvement in the optimal controller and drive-train co-design over the optimal controller for the nominal, as built, device design.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Ultra-Fast and Accurate MPPT Control Structure for Mobile PV System Under
Fast-Changing Atmospheric Conditions-
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Authors: Houssam Deboucha;Mostefa Kermadi;Saad Mekhilef;Sofia Lalouni Belaid;
Pages: 2168 - 2176
Abstract: Photovoltaic (PV) system efficiency is highly dependent on atmospheric conditions, which are continuously varying. The presence of an efficient maximum power point tracking (MPPT) method suitable for fast-changing environmental conditions, even under partial shading conditions (PSC) is required to optimize the PV system efficiency. This paper proposes a different MPPT control structure, where the MPPT is nested in the proportional-integral (PI) voltage controller routine. By using the proposed control structure. The Analog-to-Digital Converter (ADC) samples are shared between the MPPT algorithm and PI controller. As a result, extreme dynamic tracking improvements while a cost-effective implementation is ensured. Using the proposed control structure, a high-speed MPPT is achieved, making it suitable for mobile PV systems subjected to fast-changing atmospheric conditions. As the tracking accuracy is high, the P&O subroutine usually used in GMPPT methods to maintain the GMPP is eliminated. Results revealed that with the proposed implementation scheme, the tracking speed and the transient energy losses can be four times better than the traditional scheme.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- A Novel MPP Estimation Technique for DDM PV Array Under Different Solar
Irradiance Conditions-
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Authors: Shivam Tripathi;Sarthak Chopra;Himanshu Sekhar Sahu;Mahesh K. Mishra;Shashank Kumar;Sisir Kumar Nayak;
Pages: 2177 - 2191
Abstract: This article derives a new explicit expression of current of a double-diode model (DDM) photovoltaic (PV) module from the implicit current-voltage (I-V) expression. Here, the exponential term of the I-V expression of a DDM PV module is represented by Taylor series. The validation is done on the implicit I-V expression with the derived quartic I-V explicit expression for PV modules of different rating under different environmental conditions (DEC). The estimation of maximum power point (MPP) is done using one dimensional Newton Raphson (NR) algorithm in the MPP region and proposed explicit expression. The proposed technique is also compared with the different existing MPP estimation techniques for DEC. The results show better accuracy of the proposed technique over the existing methods. Finally, a new mathematical model of totally cross tied (TCT) configuration of a PV array under partially shaded conditions is derived and the global MPP is estimated using an intelligent search algorithm i.e., genetic algorithm. Further, a 315 W PV module was considered for real time validation of the proposed technique under uniform irradiance condition. Also, the calculated global MPP of a DDM PV array of TCT configuration is validated with experimental results for different shading conditions.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Optimal Battery Energy Storage Control for Multi-Service Provision Using a
Semidefinite Programming-Based Battery Model-
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Authors: Hunyoung Shin;Ross Baldick;
Pages: 2192 - 2204
Abstract: Battery energy storage systems (BESS) capable of simultaneously providing multiple grid services can assist a distribution grid operator in overcoming various challenges due to the high penetration of distributed solar photovoltaics and accelerated electrification. The seamless provision of multiple services can be ensured with BESS control decisions based on an accurate model reflecting the battery characteristics. Thus, this paper presents a novel Li-ion battery model based on linear matrix inequalities along with a semidefinite programming (SDP)-based model to determine the optimal BESS control decisions for the provision of multiple services in distribution systems. Peak shaving, power factor improvement, and electricity cost savings are considered for the services. Moreover, the mathematical analysis reveals that the original nonconvex problem can be equivalently transformed into the SDP model. The effectiveness of using BESS in the distribution system is verified through simulations using real-world data. Ultimately, a comparative analysis with a conventional linear model demonstrates that the proposed battery model reduces the energy losses in the batteries, in the simulation condition, and help maintain the battery states within normal operating limits.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Grid-Forming Services From Hydrogen Electrolyzers
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Authors: Saman Dadjo Tavakoli;Mehdi Ghazavi Dozein;Vinícius A. Lacerda;Marc Cheah Mañe;Eduardo Prieto-Araujo;Pierluigi Mancarella;Oriol Gomis-Bellmunt;
Pages: 2205 - 2219
Abstract: Hydrogen electrloyzers are power-to-gas storage devices that can facilitate large-scale integration of intermittent renewable sources into the future power systems. Due to their fast response and capability to operate in different loading conditions, they can be used as responsive loads providing support to AC grid during transients. This paper suggests taking one step further and using hydrogen electrolyzers to provide grid-forming services to the grid. As a result, the electrolyzer's role is elevated from supporting the grid (responsive load) to actively participating in forming voltage and frequency of the grid. The grid-forming capability of electrolyzer is linked to its hydrogen production constraints, which can potentially pose limitations on the grid-forming services. Besides the grid-forming mode, two additional operating modes, i.e., DC voltage mode and constant power mode, are proposed to ensure a safe operation of the electrolyzer in case of adversary interaction between grid-forming operation and hydrogen production constraints. This paper also studies the impacts of grid-forming services on the electrolyzer's physical features such as hydrogen stack temperature and efficiency. Comprehensive simulations are conducted on a low-inertia test network whose topology is inspired by a portion of the transmission grid in South Australia to confirm the effectiveness of the proposed concept under various operational conditions of the electrolyzer and upstream AC grid. Moreover, the practical feasibility of the proposed control system is experimentally validated by conducting hardware-in-the-loop tests.
PubDate: WED, 20 SEP 2023 14:07:57 -04
Issue No: Vol. 14, No. 4 (2023)
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- Chance-Constrained Joint Dispatch of Generation and Wind Curtailment-Load
Shedding Schemes With Large-Scale Wind Power Integration-
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Authors: Shuwei Xu;Wenchuan Wu;Yue Yang;Chenhui Lin;Yingshang Liu;
Pages: 2220 - 2233
Abstract: Wind curtailment (WC) and load shedding (LS) are indispensable measures to mitigate the operational risk in high-renewable power systems. Moreover, WC and LS schemes should be pre-scheduled and confirmed by related entities to make them applicable. In this article, we propose a novel chance-constrained economic dispatch (CCED) model which can generate optimal WC and LS schemes accounting for the reserve shortage and transmission congestion problems. In the proposed method, WC and LS power are formulated as random decision variables, with which the infeasibility issues of conventional CCED are fully addressed. To solve the proposed model, we first convert the complicated chance constraints into a set of deterministic inequalities equivalently by employing the conditional Value-at-Risk (CVaR) representation and duality theory. Then, a two-layer iterative algorithm is proposed to solve the equivalent problem efficiently, which is based on the generalized Benders decomposition (GBD) framework. Numerical tests demonstrate the effectiveness and efficiency of the proposed method.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Chance-Constrained Generic Energy Storage Operations Under
Decision-Dependent Uncertainty-
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Authors: Ning Qi;Pierre Pinson;Mads R. Almassalkhi;Lin Cheng;Yingrui Zhuang;
Pages: 2234 - 2248
Abstract: Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations. While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. Simulation-based analysis shows that the proposed methods yield conservative, but credible, GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Efficient Partial Shading Detection for Photovoltaic Generation Systems
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Authors: Jen-Hao Teng;Han-Cheng Wu;Zhen-Hao Wu;Wei-Hao Huang;
Pages: 2249 - 2259
Abstract: Maximum Power Point Tracking (MPPT) is the core technology for harvesting maximum power from Photovoltaic Power Generation Systems (PVGS). Global MPPT (GMPPT) algorithms have been broadly proposed in the past few years for PVGSs under Partial Shading (PS); however, most of these algorithms make the speed of MPPT slower and consequently lead to more tracking losses when there is no PS. Therefore, an efficient PS Detection (PSD) for PVGSs is proposed in the paper. The proposed PSD estimates the solar irradiance and temperature of a PVGS by the characteristic output function and measured voltages and currents. The occurrence of PS can then be detected by the estimated solar irradiance and temperature along with the proposed PS factor. After the PS condition of a PVGS was determined, the appropriate MPPTs can be chosen to track the MPP effectively and efficiently. This can accelerate MPPT performance, reduce tracking time, and improve power generation efficiency. Simulation results show that the detection accuracy of the proposed PSD can exceed 99% under different PS and temperature conditions. Experimental results demonstrate the validity and performance of the proposed PSD for PVGSs.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Decentralized Energy Management of Microgrid Based on Blockchain-Empowered
Consensus Algorithm With Collusion Prevention-
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Authors: Hongyi Li;Hongxun Hui;Hongcai Zhang;
Pages: 2260 - 2273
Abstract: The concern for privacy and scalability has motivated a paradigm shift to decentralized energy management methods in microgrids. The absence of a central authority brings significant challenges to promote trusted collaboration and avoid collusion. To address these issues, this paper proposes a blockchain-empowered microgrid energy management framework, which adopts a novel consensus-based algorithm with a collusion prevention mechanism. Aiming at social welfare maximization, the energy management problem is formulated into a convex and decomposable form, which can be solved in a decentralized manner. To prevent the collusion between malicious agents, we propose a random information transmission mechanism empowered by the blockchain smart contract to replace the time-invariant communication topology. The consensus-based algorithm is extended to obtain the optimal solution of the energy management problem on the random and time-varying communication topology. We theoretically proved that the proposed algorithm converges to the global optimal solution with a probability of 1, without violating the physical constraints of individual agents. The effectiveness of the proposed method was validated by multiple experiments, both within the simulation environment and on a hardware system.
PubDate: WED, 20 SEP 2023 14:07:57 -04
Issue No: Vol. 14, No. 4 (2023)
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- Membership-Function-Based Secondary Frequency Regulation for Distributed
Energy Resources in Islanded Microgrids With Communication Delay
Compensation-
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Authors: Jinshuo Su;Hongcai Zhang;Hui Liu;Lei Yu;Zhukui Tan;
Pages: 2274 - 2293
Abstract: Secondary frequency control is one of the most effective measures to ensure the stable operation of islanded microgrids (MGs). Most research on secondary frequency regulation has only focused on realizing steady-state operation objectives, that is, frequency restoration and power sharing. However, improving the dynamic performance of secondary frequency control is of great importance, especially in synchronous distributed energy resources. These synchronous units can introduce undesired oscillation modes, which may cause the instability conditions of MGs. To improve the dynamic performance of islanded MGs, a membership-function (MF) -based control strategy is proposed. The proposed strategy can trade-off between transient frequency regulation and frequency error elimination using the MF values calculated by the time-stamped synchronized measurements of distribution-level phasor measurement units. Besides, considering the time-varying communication delays in secondary frequency control loops, an adaptive delay compensator is proposed. The weights of the proposed compensator are updated by real-time delay measurements to compensate for the phase lag of control signals. Therefore, the adverse effect of communication delays on secondary frequency control is weakened effectively. Numerical simulations on an IEEE 34-bus system and a typical 40-bus islanded MG system demonstrate the advantages of the proposed method in the secondary frequency regulation of islanded MGs.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- A Risk-Based Planning Approach for Sustainable Distribution Systems
Considering EV Charging Stations and Carbon Taxes-
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Authors: Tayenne Dias de Lima;João Soares;Fernando Lezama;John F. Franco;Zita Vale;
Pages: 2294 - 2307
Abstract: Adopting distributed energy resources (DERs) is the key to a low-carbon future in electrical distribution systems (EDS). However, integrating DERs increases the uncertainties in the distribution system expansion planning (DSEP). Thus, the long-term DSEP faces a planning risk brought by the uncertainty of demand, electric vehicle (EV) demand, renewable production, and energy prices. Therefore, this work proposes a novel model for the multi-period planning of EDSs and DERs considering conditional value at risk (CVaR) to manage fluctuations in generation cost and carbon emissions. The proposed mathematical model aims to minimize the net present cost related to investment, operation, and risk. Unlike previous approaches, uncertain behavior of demand growth per planning period is addressed, and the risk is evaluated from two perspectives: planning costs and carbon taxes. Investments in substations, lines, renewable distributed generation, EV charging stations, and energy storage systems are considered. The uncertainties associated with the variability of renewable generation and demand are modeled through a set of scenarios. Finally, the model was evaluated using the 24 and 54-bus EDS. Thus, the proposal is a flexible tool that can be used for different purposes (e.g., carbon taxes, budget limits).
PubDate: WED, 20 SEP 2023 14:07:59 -04
Issue No: Vol. 14, No. 4 (2023)
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- Integrated Assessment of the Reliability and Frequency Deviation Risks in
Power Systems Considering the Frequency Regulation of DFIG-Based Wind
Turbines-
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Authors: Yusheng Zhao;Kaigui Xie;Changzheng Shao;Chengrong Lin;Mohammad Shahidehpour;Heng-Ming Tai;Bo Hu;Xiong Du;
Pages: 2308 - 2326
Abstract: Recently, frequency regulation strategies have been widely adopted in the operation and control of doubly fed induction generator-based wind turbines (DFIG-WTs). Thus, wind power, which is becoming an increasingly important energy source, is expected to play a significant role in both power generation and frequency regulation in modern power systems. Under such circumstances, maintaining the reliability and frequency of power systems at a designated level may be more challenging due to inherent uncertainties in wind power generation. In this article, the integrated assessment of the reliability and frequency deviation risks of power systems with a high penetration level of wind power is investigated. A multi-time scale analytical framework is proposed to calculate the integrated reliability and frequency deviation indices. The coupling between the reliability and frequency deviation is further addressed by developing a novel frequency-sensitive reliability model of the electric generator. Frequency deviations under supply/demand fluctuations and device failures are analyzed, and the power system frequency regulation process is modeled with the fuzzy adaptive virtual inertial response of DFIG-WTs and energy storage system (ESS). Furthermore, IEEE-RTS79 is used to verify the validity of the proposed model and solution method.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Active Power Control of Wind Turbine Generators Considering Equilibrium
Point Optimization Under Passive Rotor Speed Variation Mode-
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Authors: Huimin Gu;Zaiyu Chen;Qun Li;Minghui Yin;Yuchong Huo;Yun Zou;
Pages: 2327 - 2337
Abstract: With the deepening penetration of wind generation in power systems, it is urgent that wind turbine generators (WTG) can have active power control (APC) capabilities, i.e., WTG can adjust active power output according to the power command from wind farms. The improvement of APC performance is closely related to the rotor speed variation (RSV) mode and the optimal setting of stable equilibrium points (SEP). Compared with the active RSV with the same SEP, the passive RSV no longer focuses on the rotor speed tracking and has lower power response discrepancy and drive-train loads. However, limited by the realization principle, APC based on passive RSV overlooks the optimization and setting of SEP, which makes the rotor speed of WTG easily reach the bound of the variable-speed range and leads to speed overshoot and power drops. Hence, this paper proposes an APC method with additional pitch angle compensation. Consequently, WTG under passive RSV has the ability of optimizing and setting SEP. On this basis, the compensation pitch angle is dynamically optimized according to the predicted wind speed. The experimental results show that the proposed strategy can effectively reduce speed overshoot and power drops while taking full advantages of passive RSV.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Multi-Timescale Coordinated Control With Optimal Network Reconfiguration
Using Battery Storage System in Smart Distribution Grids-
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Authors: Raheel Zafar;Hemanshu R. Pota;
Pages: 2338 - 2350
Abstract: This paper focuses on the global optimality of the relaxed solutions of a multi-timescale co-optimization problem. The proposed co-optimization framework involves the multi-timescale co-optimization of distribution feeder reconfiguration with the optimal dispatch of traditional voltage regulating devices and utility-scale distributed energy resources. The on-load tap changer (OLTC) is scheduled on an hourly basis while the potential of fast-acting battery energy storage system and photovoltaic inverters is exploited by dispatching them on a 20-min basis. The optimal switching plan is computed on a daily basis. The proposed multi-timescale co-optimization model is formulated as a mixed-integer second-order cone program to achieve global optimum. The objective is to reduce power losses and improve load balancing among feeders. The proposed co-optimization framework satisfies the grid security constraints by employing the accurate DistFlow branch equations and exact linearization of the OLTC model. To ensure radiality, the limitation of widely used spanning tree constraints is addressed by combining them with single-commodity flow constraints. The simulation results demonstrate the feasibility (hence global optimality) of the relaxed solutions computed by the co-optimization framework.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Conservative Sparse Neural Network Embedded Frequency-Constrained Unit
Commitment With Distributed Energy Resources-
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Authors: Linwei Sang;Yinliang Xu;Zhongkai Yi;Lun Yang;Huan Long;Hongbin Sun;
Pages: 2351 - 2363
Abstract: The increasing penetration of distributed energy resources (DERs) will decrease the rotational inertia of the power system and further degrade the system frequency stability. To address the above issues, this article leverages the advanced neural network (NN) to learn the frequency dynamics and incorporates NN to facilitate system reliable operation. This article proposes the conservative sparse neural network (CSNN) embedded frequency-constrained unit commitment (FCUC) with converter-based DERs, including the learning and optimization stages. In the learning stage, it samples the inertia parameters, calculates the corresponding frequency, and characterizes the stability region of the sampled parameters using the convex hulls to ensure stability and avoid extrapolation. For conservativeness, the positive prediction error penalty is added to the loss function to prevent possible frequency requirement violation. For the sparsity, the NN topology pruning is employed to eliminate unnecessary connections for solving acceleration. In the optimization stage, the trained CSNN is transformed into mixed-integer linear constraints using the big-M method and then incorporated to establish the data-enhanced model. The case study verifies 1) the effectiveness of the proposed model in terms of high accuracy, fewer parameters, and significant solving acceleration; 2) the stable system operation against frequency violation under contingency.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Analysis of Subsynchronous Oscillation Caused by Multiple VSCs With
Different Dynamics Under Strong Grid Connections-
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Authors: Qiang Fu;Wenjuan Du;Haifeng Wang;Xianyong Xiao;
Pages: 2364 - 2375
Abstract: It is known that multiple voltage source converters (VSCs) with similar dynamics may induce subsynchronous oscillations (SSOs), particularly under weak grid connections. This study investigated the small-signal stability of multiple VSCs with different dynamics, utilizing both the model with default parameters and the model that accounts for deviations in VSC dynamics. The results indicates that the DC voltage control loops of the VSCs may cause growing SSOs even under strong grid connections. The direction and amplitude of the deviations are affected by the differences between the actual and default parameters. The impact of deviations is the largest when all the parameters are larger or smaller than the default parameters. However, the impact is reduced when some of the parameters are larger and others are smaller than the default parameters. On this basis, two reduced-order stability analysis methods were proposed to quickly and accurately assess the SSO risk in the power system. A sample power system with multiple VSCs is used to demonstrate and evaluate the conclusions and proposed methods.
PubDate: WED, 20 SEP 2023 14:07:57 -04
Issue No: Vol. 14, No. 4 (2023)
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- An Improved FCS-MPC Strategy for Low-Frequency Oscillation Stabilization
of PV-Based Microgrids-
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Authors: Zhuoli Zhao;Shaoqing Gong;Qinggang Yang;Jindian Xie;Xi Luo;Jiexiong Zhang;Qiang Ni;Loi Lei Lai;
Pages: 2376 - 2390
Abstract: With the rapid promotion of photovoltaic (PV) power generation, the local consumption of distributed PV power generation at medium and low voltage levels widely forms PV-based microgrids. The continuous increase in the penetration rate of uncertain PV power generation requires it to actively participate in the frequency and voltage regulation of weak microgrid systems. Finite control set-model predictive control (FCS-MPC) for voltage source converter (VSC) shows remarkable advantages in fast dynamic response and robustness compared with traditional cascaded linear control methods. However, the traditional FCS-MPC method for VSC without considering the DC-link voltage dynamics of the PV generators will face oscillations of power and frequency in PV-based microgrids. In this paper, to suppress the low-frequency oscillation issue caused by PV generations, an improved FCS-MPC (I-FCS-MPC) strategy considering the DC-link dynamics of PV generation is proposed, in which the AC side and DC side of the VSC are comprehensively considered to enhance the dynamic performance of the PV-based microgrids. Moreover, a DC-link voltage regulation mechanism is introduced, enabling the PV generators to operate in the maximum power point tracking (MPPT) mode. Finally, experiments are conducted to verify the effectiveness of the proposed control strategy comprehensively.
PubDate: WED, 20 SEP 2023 14:07:59 -04
Issue No: Vol. 14, No. 4 (2023)
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- Fault Ride-Through Strategies for Synchronverter-Interfaced Energy
Resources Under Asymmetrical Grid Faults-
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Authors: Saad Pola;Maher Azzouz;Ahmed S. A. Awad;Hatem Sindi;
Pages: 2391 - 2405
Abstract: Synchronverters are inverters controlled to behave like synchronous generators. However, unbalanced faults or voltage dips may stimulate synchronverters to generate excessive currents and cause instantaneous active and reactive power oscillations at twice the grid's fundamental frequency. Therefore, this article proposes three fault ride-through (FRT) strategies to remedy these power oscillations and the unlimited current generation during unbalanced faults. The first strategy is based on an instantaneous active and reactive control that significantly reduces power oscillations. The second strategy relies on an average active and reactive control that ensures the delivery of average active and reactive power and maintains sinusoidal phase currents. Finally, the third FRT strategy employs a comprehensive instantaneous active and reactive control to eliminate power oscillations and maintain sinusoidal currents simultaneously. The proposed FRT strategies are equipped with a power management controller that delivers the required active and reactive power during normal operation and limits the inverter's phase currents during faults. Further, the proposed FRT strategies maintain the intrinsic features of synchronverters and ensure seamless activation of the proposed FRT strategies. Comparative results confirm the efficacy of the proposed strategies to reduce power oscillations, limit current generation, maintain synchronverters’ intrinsic features, and comply with grid codes.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Multi-Agent Reinforcement Learning Control of a Hydrostatic Wind
Turbine-Based Farm-
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Authors: Yubo Huang;Shuyue Lin;Xiaowei Zhao;
Pages: 2406 - 2416
Abstract: This paper leverages multi-agent reinforcement learning (MARL) to develop an efficient control system for a wind farm comprising a new type of wind turbines with hydrostatic transmission. The primary motivation for hydrostatic wind turbines (HWT) is increased reliability, and reduced manufacturing, operating, and maintaining costs by removing troublesome components and reducing nacelle weight. Nevertheless, the high system complexity of HWT and the wake effect pose significant challenges for the control of HWT-based wind farms. We therefore propose a MARL algorithm named multi-agent policy optimization (MAPO), which allows agents (turbines) to gradually improve their control policies by repeatedly interacting with the environment to learn an optimal operation curve for wind farms. Simulation results based on a wind farm simulator, FAST.Farm, show that MAPO outperforms the greedy policy and a popular learning-based method, multi-agent deep deterministic policy gradient (MADDPG), in terms of power generation.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Anomaly Detection and Classification Method for Wind Speed Data of Wind
Turbines Using Spatiotemporal Dependency Structure-
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Authors: Yang Li;Xiaojun Shen;
Pages: 2417 - 2431
Abstract: Detection of anomalous wind speed time series in measurements is considered crucial for the operation and maintenance of wind speed sensors on wind turbines, as they generally reveal some inherent defects of sensors or extreme environmental conditions in wind farms. In this article, an anomaly detection and classification method termed detectable spatial and temporal dependency structure (DSTDS) is developed for wind speed data. The main idea is to transform wind speed data into dependency matrix, and then, the problems of anomaly detection are turned into dependency modelling and cross-verification. First, the attributes of raw wind speed data are analyzed and the abnormal data are classified into three categories from statistical perspective. Next, a temporal segmentation scheme for wind speed data using change-points algorithm is proposed to improve both the accuracy and sensitivity of anomaly detection. Then, the spatial dependency structure of wind speed series, which is captured by t-copula-based method, is employed to detect the fault data and weak fault data. The temporal context relation is used to formulate the velocity constraints to detect the noisy data. Finally, experiments for anomaly detection are conducted on simulation data and real-world data to validate effectiveness and universality of the proposed method.
PubDate: WED, 20 SEP 2023 14:07:59 -04
Issue No: Vol. 14, No. 4 (2023)
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- An Optimal Operation Strategy for Collaborative Flexibility Provision of a
Carbon Capture and Utilization Process With Wind Energy-
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Authors: Arash E. Samani;Nezmin Kayedpour;Farjam Kayedpour;Jeroen D. M. De Kooning;Guillaume Crevecoeur;Lieven Vandevelde;
Pages: 2432 - 2444
Abstract: Improving power system flexibility by responsive demand is essential for integrating wind energy with a high level of variability in power systems. Carbon dioxide-based chemical processes as energy-intensive industrial loads may offer a vast potential of new forms of flexible operation due to their existing control infrastructure and storage capabilities. However, a collaborative decision model is needed for optimal energy sharing among the chemical plant and the grid under the variations and uncertainties of wind power. This study develops an optimal two-stage stochastic programming model for a novel flexible operation strategy of the chemical process coupled with wind turbines. In the proposed control scheme, a small-scale wind farm provides the power input of a chemical plant. Wind turbines are connected to the grid and actively participate in the day-ahead energy and reserve markets, considering the chemical plant as a source of flexibility. An equivalent scenario-based model of the proposed optimization problem is suggested using the Group Method of Data Handling (GMDH) for a data-driven prediction of stochastic variables. Simulation results demonstrate the effectiveness and significance of the proposed approach for an optimal and collaborative contribution in ancillary market of a carbon dioxide-based chemical plant supplied by wind energy.
PubDate: WED, 20 SEP 2023 14:07:58 -04
Issue No: Vol. 14, No. 4 (2023)
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- Conditional Variational Autoencoder Informed Probabilistic Wind Power
Curve Modeling-
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Authors: Zhong Zheng;Luoxiao Yang;Zijun Zhang;
Pages: 2445 - 2460
Abstract: In this article, a conditional variational autoencoder based method is proposed for the probabilistic wind power curve modeling task. To advance the modeling performance, the latent random variable is introduced to characterize underlying weather and wind turbine conditions. The infinite Gaussian mixture model is adopted to better model the asymmetric and heterogeneous conditional distribution of the wind power given the wind speed. The conditional variational autoencoder is composed of an encoder and a decoder network. The encoder infers the state of the latent random variable given the wind speed and wind power, while the decoder learns the observational conditional distribution of the wind power given the wind speed and latent variable. With a well-trained conditional variational autoencoder, the conditional probability density function of the wind power could be estimated through the decoder network by sampling the latent random variable from its prior distribution. Wind turbine supervisory control and data acquisition datasets are used in experiments to validate advantages of the proposed method. Experimental results show that the proposed method outperforms other benchmarking deterministic and probabilistic wind power curve models with the lower continuous ranked probability score and more reliable and sharper prediction intervals. Experiments also reflect the better robustness of the conditional variational autoencoder to data pre-processed using univariate or multivariate inputs, as well as its superiority and potential for the wind power estimation considering multivariate inputs.
PubDate: WED, 20 SEP 2023 14:07:57 -04
Issue No: Vol. 14, No. 4 (2023)
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Pages: 2461 - 2461
PubDate: WED, 20 SEP 2023 14:06:37 -04
Issue No: Vol. 14, No. 4 (2023)
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Pages: 2462 - 2462
PubDate: WED, 20 SEP 2023 14:06:37 -04
Issue No: Vol. 14, No. 4 (2023)
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Pages: 2463 - 2504
PubDate: WED, 20 SEP 2023 14:06:37 -04
Issue No: Vol. 14, No. 4 (2023)
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